Abstract
The cognitive radio wireless sensor network (CR-WSN) has gained worldwide attention in recent years for its potential applications. Reliable spectrum sensing is the premise for opportunistic access to sensor nodes. However, as a result of the radio frequency (RF) front-end nonlinearity of sensor nodes, distortion products can easily degrade the spectrum sensing performance by causing false alarms and degrading the detection probability. Given the limitations of the widely-used adaptive interference cancellation (AIC) algorithm, this paper develops several details to avoid these limitations and form a new mitigation architecture to alleviate nonlinear distortions. To demonstrate the efficiency of the proposed algorithm, verification tests for both simulations and actual RF front-end measurements are presented and discussed. The obtained results show that distortions can be suppressed significantly, thus improving the reliability of spectrum sensing. Moreover, compared to AIC, the proposed algorithm clearly shows better performance, especially at the band edges of the interferer signal.
Highlights
The proliferation of Micro-Electro-Mechanical Systems (MEMS) technology has facilitated the development of smart sensors, enabling wireless sensor networks (WSNs) by providing smaller, cheaper, and more intelligent sensors [1]
Current WSNs operate in the Industrial Scientific Medical (ISM) bands, which are shared by many other successful communication technologies
radio frequency (RF) front-end is illustrated in Figure or homodyne architectures, direct-digitization front-end can be adopted in order to reduce due to the low cost and resource-constrained natureRF
Summary
The proliferation of Micro-Electro-Mechanical Systems (MEMS) technology has facilitated the development of smart sensors, enabling wireless sensor networks (WSNs) by providing smaller, cheaper, and more intelligent sensors [1]. Rebeiz [19] was one of the first papers to study the impact of nonlinearities on spectrum sensing performance This was subsequently followed by [20], which first analytically derived the theoretical false alarm and detection probabilities in closed form, for both energy and cyclostationary detection in nonlinear front-ends. [16,23], used this approach to improve the reliability of spectrum sensing This approach is based on the principle of feed forward mitigation, which regenerates distortions with the help of a reference model and adaptively subtracts them from the received signal. Cancellation of in-band nonlinear distortions on the blocker bands as well as on the bandstop filter the use of bandstop filters in AIC, for stable coefficient adaptation, prevents the cancellation of transition bands.
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